Abstract
In the case of motor bearing failure, aiming at the weak bearing fault characteristics in stator current, this paper takes advantage of the sensitivity of cyclic autocorrelation function for initial fault characteristics and proposes a motor bearing fault detection method based on cyclic autocorrelation function analysis. Firstly, according to the torque fluctuation model, the stator current expression of bearing outer raceway fault is deduced. Then, the current expression is added to the cyclic autocorrelation function, and the result shows that the proposed method can not only demodulate the fault characteristic frequency, but also the amplitude of the fault characteristic signal will be maximized by selecting the appropriate slicing position, which is beneficial to the fault identification. Finally, the experimental platform is built, and the fault characteristics are extracted effectively by using the proposed method, and its performance is obviously better than that of the traditional power spectrum analysis.
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Acknowledgements
The research work was supported by National Natural Science Foundation of China under Grant No. 51279020. The support is greatly appreciated.
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Wang, P., Qiu, C., Wu, X., Xue, Z. (2020). Research on Motor Bearing Fault Detection Method Based on Cyclic Autocorrelation Function Analysis. In: Jia, L., Qin, Y., Liu, B., Liu, Z., Diao, L., An, M. (eds) Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019. EITRT 2019. Lecture Notes in Electrical Engineering, vol 638. Springer, Singapore. https://doi.org/10.1007/978-981-15-2862-0_16
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DOI: https://doi.org/10.1007/978-981-15-2862-0_16
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